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Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.

GAN.tar.gz

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This course is an introduction to Generative Adversarial Networks (GANs) and a practical step-by-step tutorial on making your own with PyTorch. Through this course, you will learn how to build GANs with industry-standard tools.

In the first section, you will dive into PyTorch and refresh your understanding of neural networks by building a simple image classifier. In the second section of this course, you will explore the idea of adversarial training and build progressively more sophisticated GANs; first by learning a simple 1010 pattern, then monochrome images of handwritten digits, and finally full-color images of faces.

The third, and last section, extends the core GAN concept, applying it to convolutional neural networks, and developing a conditional GAN for generating data of the desired class.

By the end of this course, you will have a solid understanding of how to build GANs for your machine learning projects.

Make Your First GAN Using PyTorch

# MNIST dataset

The official source of the MNIST dataset is at Yann leCun’s website:

- http://yann.lecun.com/exdb/mnist/index.html

> 📝 The data is available under a Creative Commons Attribution-Share Alike 3.0 license:
>
>- https://creativecommons.org/licenses/by-sa/3.0/



# CelebA dataset

The source of the CelebA dataset is at the Multimedia Laboratory, The Chinese University of Hong Kong:

- http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

> 📝 The dataset is for non-commercial research and educational purposes only. Permission has been obtained for this course to provide instructions for working with the dataset. Images from the dataset won’t be reproduced here, and only GAN generated images will be shown.

# The VGGFACE2 dataset

The dataset for recognizing face across pose and age is taken from:

- https://www.robots.ox.ac.uk/~vgg/data/vgg_face/

>  📝 The data is available under a Creative Commons Attribution-Share Alike 3.0 license:
>
>- https://creativecommons.org/licenses/by-sa/3.0/


# Fashion MNIST dataset

The dataset for recognizing fashion mnist images is taken from kaggle.

* https://www.kaggle.com/zalando-research/fashionmnist



# Handwritten Alphabets dataset

The dataset for recognizing fashion mnist images is taken from kaggle.

- https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format


# NVIDIA and Google

NVIDIA® and CUDA™ are registered trademarks of NVIDIA®.

CoLaboratory™, often referred to as Colab, is a registered trademark of Google. Google and the Google logo are registered trademarks of Google LLC.


# Open source

A big thank you to the open-source community, without which we would never have such amazing software to use, knowledge to share, and friends to support and inspire us.

_______ 


Acknowledgment of all the resources used in this course.

Appendix E: Acknowledgments

✨Introduction

✨PyTorch Basics

🖥️ Project: Computation Graph and Gradient Calculation

✨First PyTorch Neural Network

✨Refinements

🖥️ Project: Fashion MNIST Classification Model

✨CUDA Basics

Make Your First GAN: Part 1 - Exam

✨The GAN Idea

✨Simple 1010 Pattern

🖥️ Project: A Simple 01110 Pattern GAN

✨Handwritten Digits

🖥️ Project: Fashion MNIST GAN

✨Human Faces

Make Your First GAN: Part 2 - Exam

✨Convolutional GANs

🖥️ Project: A Fashion MNIST CNN

✨Conditional GANs

🖥️ Project: Fashion MNIST Conditional GAN

Make Your First GAN: Part 3 - Exam

✨Conclusion

✨Appendix

Appendix E: Acknowledgments

MNIST dataset